A DEVELOPMENT FOR ESTIMATING OF FLOWING GROUNDWATER WITH A CONVOLUTIONAL NEURAL NETWORK

Abstract

 To detect the flowing groundwater with high accuracy using 1 m Depth Temprature, skilled technology based on many years of experience is required. Therefore, in this study, we developed a Convolutional Neural Network model that estimates the planar position of the flowing groundwater from the results of 1 m Depth Temprature survey. Numerical simulation was performed to make typical thermal condition under the ground. And a data set used for supervised learning was created from results of the numerical simulation. By learning of the Convolutional Neural Network model with the result of the numerical simulation, The Convolutional Neural Network model showed 99.7 % accuracy. Additionally, in order to confirm the Convolutional Neural Network system, the estimated route by the Convolutional Neural Network model was compared with the estimated route by the engineer. Results of the comparison showed that the Convolutional Neural Network has good reproducibility.